OpenRaiser/Intern-Atlas huggingface dataset:
Evolutionary Multimodal Optimization: A Short Survey
Venue: arXiv cs.NE 2015
Year: 2015

Real world problems always have different multiple solutions. For instance, optical engineers need to tune the recording parameters to get as many optimal solutions as possible for multiple trials in the varied-line-spacing holographic grating design problem. Unfortunately, most traditional optimization techniques focus on solving for a single optimal solution. They need to be applied several times; yet all solutions are not guaranteed to be found. Thus the multimodal optimization problem was proposed. In that problem, we are interested in not only a single optimal point, but also the others. With strong parallel search capability, evolutionary algorithms are shown to be particularly effective in solving this type of problem. In particular, the evolutionary algorithms for multimodal optimization usually not only locate multiple optima in a single run, but also preserve their population diversity throughout a run, resulting in their global optimization ability on multimodal functions. In addition, the techniques for multimodal optimization are borrowed as diversity maintenance techniques to other problems. In this chapter, we describe and review the state-of-the-arts evolutionary algorithms for multimodal optimization in terms of methodology, benchmarking, and application.
A combinatorial approach to X-tolerant compaction circuits
Venue: arXiv cs.IT 2015
Year: 2015

Test response compaction for integrated circuits (ICs) with scan-based design-for-testability (DFT) support in the presence of unknown logic values (Xs) is investigated from a combinatorial viewpoint. The theoretical foundations of X-codes, employed in an X-tolerant compaction technique called X-compact, are examined. Through the formulation of a combinatorial model of X-compact, novel design techniques are developed for X-codes to detect a specified maximum number of errors in the presence of a specified maximum number of unknown logic values, while requiring only small fan-out. The special class of X-codes that results leads to an avoidance problem for configurations in combinatorial designs. General design methods and nonconstructive existence theorems to estimate the compaction ratio of an optimal X-compactor are also derived.
A Weakly Supervised Learning Approach based on Spectral Graph-Theoretic Grouping
Venue: arXiv cs.LG 2015
Year: 2015

In this study, a spectral graph-theoretic grouping strategy for weakly supervised classification is introduced, where a limited number of labelled samples and a larger set of unlabelled samples are used to construct a larger annotated training set composed of strongly labelled and weakly labelled samples. The inherent relationship between the set of strongly labelled samples and the set of unlabelled samples is established via spectral grouping, with the unlabelled samples subsequently weakly annotated based on the strongly labelled samples within the associated spectral groups. A number of similarity graph models for spectral grouping, including two new similarity graph models introduced in this study, are explored to investigate their performance in the context of weakly supervised classification in handling different types of data. Experimental results using benchmark datasets as well as real EMG datasets demonstrate that the proposed approach to weakly supervised classification can provide noticeable improvements in classification performance, and that the proposed similarity graph models can lead to ultimate learning results that are either better than or on a par with existing similarity graph models in the context of spectral grouping for weakly supervised classification.
Connectivity in Secure Wireless Sensor Networks under Transmission Constraints
Venue: arXiv cs.CR 2015
Year: 2015

In wireless sensor networks (WSNs), the Eschenauer-Gligor (EG) key pre-distribution scheme is a widely recognized way to secure communications. Although connectivity properties of secure WSNs with the EG scheme have been extensively investigated, few results address physical transmission constraints. These constraints reflect real-world implementations of WSNs in which two sensors have to be within a certain distance from each other to communicate. In this paper, we present zero-one laws for connectivity in WSNs employing the EG scheme under transmission constraints. These laws help specify the critical transmission ranges for connectivity. Our analytical findings are confirmed via numerical experiments. In addition to secure WSNs, our theoretical results are also applied to frequency hopping in wireless networks.
Iterative detection and decoding for SCMA systems with LDPC codes
Venue: arXiv cs.IT 2015
Year: 2015

Sparse code multiple access (SCMA) is a promising multiplexing approach to achieve high system capacity. In this paper, we develop a novel iterative detection and decoding scheme for SCMA systems combined with Low-density Parity-check (LDPC) decoding. In particular, we decompose the output of the message passing algorithm (MPA) based SCMA multiuser detection into intrinsic part and prior part. Then we design a joint detection and decoding scheme which iteratively exchanges the intrinsic information between the detector and the decoder, yielding a satisfied performance gain. Moreover, the proposed scheme has almost the same complexity compared to the traditional receiver for LDPC-coded SCMA systems. As numerical results demonstrate, the proposed scheme has a substantial gain over the traditional SCMA receiver on AWGN channels and Rayleigh fading channels.
Interval Predictability in Discrete Event Systems
Venue: arXiv cs.SY 2015
Year: 2015

In this paper we study the problem of predictability in partially observable discrete event systems, i.e., the question whether an observer can predict the occurrence of a fault. We extend the definition of predictability to consider the time interval where the fault will occur: the $(i,j)$-predictability does not only specify that the fault will be predicted before it occurs, but also that the predictor will be able to predict that its occurrence will occur in $i$ to $j$ observations from now. We also provide a quadratic algorithm that decides predictability of the system.
Deterministic Differential Search Algorithm for Distributed Sensor/Relay Networks
Venue: arXiv cs.IT 2015
Year: 2015

For distributed sensor/relay networks, high reliability and power efficiency are often required. However, several implementation issues arise in practice. One such problem is that all the distributed transmitters have limited power supply since the power source of the transmitters cannot be recharged continually. To resolve this, distributed beamforming has been proposed as a viable solution where all distributed transmitters seek to align in phase at the receiver end. However, it is difficult to implement such transmit beamforming in a distributed fashion in practice since perfect channel state information (CSI) need to be made available at all distributed transmitters, requiring tremendous overhead to feed back CSI from the receiver to all distributed transmitters. In this paper, we propose a novel algorithm that belongs to the category of deterministic phase adjustment algorithm: the Deterministic Differential Search Algorithm (DDSA), where the differences between the measured received signal strength (RSS) are utilized judiciously to help us predict the deterministic phase adjustment done at distributed transmitters. Numerical simulations demonstrate rapid convergence to a pre-determined threshold.
Multi-Label Active Learning from Crowds
Venue: arXiv cs.LG 2015
Year: 2015

Multi-label active learning is a hot topic in reducing the label cost by optimally choosing the most valuable instance to query its label from an oracle. In this paper, we consider the poolbased multi-label active learning under the crowdsourcing setting, where during the active query process, instead of resorting to a high cost oracle for the ground-truth, multiple low cost imperfect annotators with various expertise are available for labeling. To deal with this problem, we propose the MAC (Multi-label Active learning from Crowds) approach which incorporate the local influence of label correlations to build a probabilistic model over the multi-label classifier and annotators. Based on this model, we can estimate the labels for instances as well as the expertise of each annotator. Then we propose the instance selection and annotator selection criteria that consider the uncertainty/diversity of instances and the reliability of annotators, such that the most reliable annotator will be queried for the most valuable instances. Experimental results demonstrate the effectiveness of the proposed approach.
Information Cascades and Online Rating Games
Venue: arXiv cs.SI 2015
Year: 2015

Through mathematical analysis and simulations, online ratings and their impact on businesses are characterized through two parameters: an inherent and objective restaurant quality factor, and the accuracy of customers' gut feeling about a business. Within this model, it is found that online ratings are seldom accurate mainly because of the low or high accuracy in customers' gut feelings.
A review of heterogeneous data mining for brain disorders
Venue: arXiv cs.LG 2015
Year: 2015

With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses many unique challenges for data mining research. For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity and nonlinear separability. Furthermore, brain connectivity networks can be constructed from the tensor data, embedding subtle interactions between brain regions. Other clinical measures are usually available reflecting the disease status from different perspectives. It is expected that integrating complementary information in the tensor data and the brain network data, and incorporating other clinical parameters will be potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, multi-view feature analysis. In this paper, we review some recent data mining methods that are used for analyzing brain disorders.
A Review of Technical Problems when Conducting an Investigation in Cloud Based Environments
Venue: arXiv cs.CR 2015
Year: 2015

Cloud computing is a relatively new technology which is quickly becoming one of the most important technological advances for computer science. This technology has had a significant growth in recent years. It is now more affordable and cloud platforms are becoming more stable. Businesses are successfully migrating their systems to a cloud infrastructure, obtaining technological and economic benefits. However, others still remain reluctant to do it due to both security concerns and the loss of control over their infrastructures and data that the migration entails. At the same time that new technologies progress, its benefits appeal to criminals too. They can not only steal data from clouds, but they can also hide data in clouds, which has provoked an increased in the number of cybercrimes and their economic impacts. Their victims range from children and adults to companies and even countries. On the other hand, digital forensics have negatively suffered the impact of the boom of cloud computing due to its dynamic nature. The tools and procedures that were successfully proved and used in digital investigations are now becoming irrelevant, making it an urging necessity to develop new forensics capabilities for conducting an investigation in this new environment. As a consequence of these needs a new area has emerged, Cloud Forensics, which is the result of the intersection between cloud computing and digital forensics. Keywords: Cloud forensics, cloud computing, forensics investigation, forensic challenges.
Estimating snow cover from publicly available images
Venue: arXiv cs.MM 2015
Year: 2015

In this paper we study the problem of estimating snow cover in mountainous regions, that is, the spatial extent of the earth surface covered by snow. We argue that publicly available visual content, in the form of user generated photographs and image feeds from outdoor webcams, can both be leveraged as additional measurement sources, complementing existing ground, satellite and airborne sensor data. To this end, we describe two content acquisition and processing pipelines that are tailored to such sources, addressing the specific challenges posed by each of them, e.g., identifying the mountain peaks, filtering out images taken in bad weather conditions, handling varying illumination conditions. The final outcome is summarized in a snow cover index, which indicates for a specific mountain and day of the year, the fraction of visible area covered by snow, possibly at different elevations. We created a manually labelled dataset to assess the accuracy of the image snow covered area estimation, achieving 90.0% precision at 91.1% recall. In addition, we show that seasonal trends related to air temperature are captured by the snow cover index.
Encoding CSP into CCS (Extended Version)
Venue: arXiv cs.LO 2015
Year: 2015

We study encodings from CSP into asynchronous CCS with name passing and matching, so in fact, the asynchronous pi-calculus. By doing so, we discuss two different ways to map the multi-way synchronisation mechanism of CSP into the two-way synchronisation mechanism of CCS. Both encodings satisfy the criteria of Gorla except for compositionality, as both use an additional top-level context. Following the work of Parrow and Sjödin, the first encoding uses a central coordinator and establishes a variant of weak bisimilarity between source terms and their translations. The second encoding is decentralised, and thus more efficient, but ensures only a form of coupled similarity between source terms and their translations.
Evaluation des Algorithmus DIBADAWN zum Detektieren von Bruecken und Gelenkpunkten in 802.11 Maschennetzen
Venue: arXiv cs.NI 2015
Year: 2015

The topic is the evaluation of the algorithm DIBADAWN to detect bridges and articulation points in an wireless mess network. Therefore this algorithm is implemented and evaluated per simulation.
Vehicle to Vehicle Authentication
Venue: arXiv cs.CR 2015
Year: 2015

In recent future, vehicles will establish a spontaneous connection over a wireless radio channel, coordinating actions and information. Vehicles will exchange warning messages over the wireless radio channel through Dedicated Short Range Communication (IEEE 1609) over the Wireless Access in Vehicular Environment (802.11p). Unfortunately, the wireless communication among vehicles is vulnerable to security threats that may lead to very serious safety hazards. Therefore, the warning messages being exchanged must incorporate an authentic factor such that recipient is willing to verify and accept the message in a timely manner
Degree-constrained Subgraph Reconfiguration is in P
Venue: arXiv cs.DM 2015
Year: 2015

The degree-constrained subgraph problem asks for a subgraph of a given graph such that the degree of each vertex is within some specified bounds. We study the following reconfiguration variant of this problem: Given two solutions to a degree-constrained subgraph instance, can we transform one solution into the other by adding and removing individual edges, such that each intermediate subgraph satisfies the degree constraints and contains at least a certain minimum number of edges? This problem is a generalization of the matching reconfiguration problem, which is known to be in P. We show that even in the more general setting the reconfiguration problem is in P.
Referencing Tool for Reputation and Trust in Wireless Sensor Networks
Venue: arXiv cs.NI 2015
Year: 2015

Presently, there are not many literatures on the characterization of reputation and trust in wireless sensor networks (WSNs) which can be referenced by scientists, researchers and students. Although some research documents include information on reputation and trust, characterization of these features are not adequately covered. In this paper, reputation and trust are divided into various classes or categories and a method of referencing the information is provided. This method used results in providing researchers with a tool that makes it easier to reference these features on reputation and trust in a much easier way than if referencing has to be directed to several uncoordinated resources. Although the outcome of this work proves beneficial to research in the characterization of reputation and trust in WSNs, more work needs to be done in extending the benefits to other network systems.
Automatic 3D Liver Segmentation Using Sparse Representation of Global and Local Image Information via Level Set Formulation
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, a novel framework for automated liver segmentation via a level set formulation is presented. A sparse representation of both global (region-based) and local (voxel-wise) image information is embedded in a level set formulation to innovate a new cost function. Two dictionaries are build: A region-based feature dictionary and a voxel-wise dictionary. These dictionaries are learned, using the K-SVD method, from a public database of liver segmentation challenge (MICCAI-SLiver07). The learned dictionaries provide prior knowledge to the level set formulation. For the quantitative evaluation, the proposed method is evaluated using the testing data of MICCAI-SLiver07 database. The results are evaluated using different metric scores computed by the challenge organizers. The experimental results demonstrate the superiority of the proposed framework by achieving the highest segmentation accuracy (79.6\%) in comparison to the state-of-the-art methods.
Remark on the hypothetical judgment
Venue: arXiv cs.LO 2015
Year: 2015

What is the proper explanation of intuitionistic hypothetical judgment, and thence propositional implication? The answer is unclear from the writings of Brouwer and Heyting, who in their lifetimes propounded multiple (sometimes conflicting) explanations of the hypothetical judgment. To my mind, the determination of an acceptable explanation must take into account its adequacy for the expression of the bar theorem and, more generally, the development of an open-ended framework for transcendental arguments in mathematics.
Shared-object System Equilibria: Delay and Throughput Analysis
Venue: arXiv cs.DC 2015
Year: 2015

We consider shared-object systems that require their threads to fulfill the system jobs by first acquiring sequentially the objects needed for the jobs and then holding on to them until the job completion. Such systems are in the core of a variety of shared-resource allocation and synchronization systems. This work opens a new perspective to study the expected job delay and throughput analytically, given the possible set of jobs that may join the system dynamically. We identify the system dependencies that cause contention among the threads as they try to acquire the job objects. We use these observations to define the shared-object system equilibria. We note that the system is in equilibrium whenever the rate in which jobs arrive at the system matches the job completion rate. These equilibria consider not only the job delay but also the job throughput, as well as the time in which each thread blocks other threads in order to complete its job. We then further study in detail the thread work cycles and, by using a graph representation of the problem, we are able to propose procedures for finding and estimating equilibria, i.e., discovering the job delay and throughput, as well as the blocking time. To the best of our knowledge, this is a new perspective, that can provide better analytical tools for the problem, in order to estimate performance measures similar to ones that can be acquired through experimentation on working systems and simulations, e.g., as job delay and throughput in (distributed) shared-object systems.
Distribution of maximal clique size of the vertices for theoretical small-world networks and real-world networks
Venue: arXiv cs.SI 2015
Year: 2015

Our primary objective in this paper is to study the distribution of the maximal clique size of the vertices in complex networks. We define the maximal clique size for a vertex as the maximum size of the clique that the vertex is part of and such a clique need not be the maximum size clique for the entire network. We determine the maximal clique size of the vertices using a modified version of a branch-and-bound based exact algorithm that has been originally proposed to determine the maximum size clique for an entire network graph. We then run this algorithm on two categories of complex networks: One category of networks capture the evolution of small-world networks from regular network (according to the wellknown Watts-Strogatz model) and their subsequent evolution to random networks; we show that the distribution of the maximal clique size of the vertices follows a Poisson-style distribution at different stages of the evolution of the small-world network to a random network; on the other hand, the maximal clique size of the vertices is observed to be in-variant and to be very close to that of the maximum clique size for the entire network graph as the regular network is transformed to a small-world network. The second category of complex networks studied are real-world networks (ranging from random networks to scale-free networks) and we observe the maximal clique size of the vertices in five of the six real-world networks to follow a Poisson-style distribution. In addition to the above case studies, we also analyze the correlation between the maximal clique size and clustering coefficient as well as analyze the assortativity index of the vertices with respect to maximal clique size and node degree.
A Method in Security of Wireless Sensor Network based on Optimized Artificial immune system in Multi-Agent Environments
Venue: arXiv cs.NI 2015
Year: 2015

Security in computer networks is one of the most interesting aspects of computer systems. It is typically represented by the initials CIA: confidentiality, integrity, and authentication or availability. Although, many access levels for data protection have been identified in computer networks, the intruders would still find lots of ways to harm sites and systems. The accommodation proceedings and the security supervision in the network systems, especially wireless sensor networks have been changed into a challenging point. One of the newest security algorithms for wireless sensor networks is Artificial Immune System (AIS) algorithm. Human lymphocytes play the main role in recognizing and destroying the unknown elements. In this article, we focus on the inspiration of these defective systems to guarantee the complications security using two algorithms; the first algorithms proposed to distinguish self-nodes from non-self ones by the related factors and the second one is to eliminate the enemy node danger.The results showed a high rate success and good rate of detecting for unknown object; it could present the best nodes with high affinity and fitness to be selected to confront the unknown agents.